Pretreatment CT and ¹⁸F‐FDG PET‐based radiomic model predicting pathological complete response and loco‐regional control following neoadjuvant chemoradiation in oesophageal cancer

Abstract

Introduction To develop a radiomic‐based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer. Methods We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in‐house data‐chjmirocterization algorithm was used to extract 3D‐radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, Fc = α * FPET + (1 − α) * FCT, 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco‐regional control (LRC), recurrence‐free survival (RFS), metastasis‐free survival (MFS) and overall survival (OS) were estimated by Kaplan–Meier analysis, and compared using log‐rank test. Results Median follow‐up was 59 months. pCR was achieved in 34 (50%) patients. Five‐year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0–1 indicative of complete response or minimal residual disease was significantly associated with improved 5‐year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04–0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5‐year LRC favouring low‐score group (91.1% vs. 80%, 95% CI 0.09–1.77, P = 0.2). Conclusion The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

“…The second-order class includes the graylevel (GL) co-occurrence matrix, GL run-length matrix, GL size-zone matrix, GL distance-zone matrix, neighborhood gray tone difference matrix, and neighboring GL dependence matrix (8). We reviewed the radiomics feature type used and other types of features of the selected articles, and the results are provided in Table III (19), four studies used the first-order feature (17,(20)(21)(22), and five studies used the second-order feature (14,17,(21)(22)(23).…”

“…The details of the algorithms used in the selected studies are provided in Table IV. Of the selected studies, three used machine learning algorithms (LASSO and SVM) (14,17,21), while the others used traditional biostatistical methods (19,20,22,23).…”

18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

“…One study used the morphology class feature ( 20 ) while three studies employed ( 21 , 22 , 24 ) the second-order class including different grey-level matrix (i.e., grey-level run-length matrix, grey-level co-occurrence matrix, grey-level size-zone matrix, grey-level dependence matrix); lastly, Rishi et al. ( 23 ) used both the first-order (i.e., intensity and shape) and second-order classes.…”

“…We included a total of five studies: three carried out in the USA ( 20 , 21 , 23 ), one in the Netherlands ( 22 ), and one in Japan ( 24 ).…”

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

“…The emerging field of "radiomics" has great potential in disease diagnosis, prognosis evaluation, and prediction of treatment (15). It successfully showed favorable abilities in clinical management (16)(17)(18)(19). However, no studies have used the PET-based radiomics tool to predicting the PM of GC.…”